References

The following references identify the works and standards cited in the report. Web resources were checked during preparation on 1 October 2026. Preprints are labeled explicitly. Links identify the cited record or an accessible original version; they do not imply that this report reproduced the paper's experiments.

1. [1]
   Hogan, A., et al. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4).Peer reviewed survey
2. [2]
   Ji, S., Pan, S., Cambria, E., Marttinen, P., and Yu, P. S. (2022). A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2), 494–514.Peer reviewed survey
3. [3]
   Zhong, L., Wu, J., Li, Q., Peng, H., and Wu, X. (2024; online 2023). A Comprehensive Survey on Automatic Knowledge Graph Construction. ACM Computing Surveys, 56.Peer reviewed survey
4. [4]
   W3C (2014). RDF 1.1 Concepts and Abstract Syntax.W3C Recommendation
5. [5]
   W3C (2014). RDF 1.1 Semantics.W3C Recommendation
6. [6]
   W3C (2014). RDF Schema 1.1.W3C Recommendation
7. [7]
   W3C (2012). OWL 2 Web Ontology Language Primer, Second Edition.W3C Recommendation
8. [8]
   W3C (2017). Shapes Constraint Language, SHACL.W3C Recommendation
9. [9]
   W3C (2013). SPARQL 1.1 Query Language.W3C Recommendation
10. [10]
    W3C (2013). PROV-O: The PROV Ontology.W3C Recommendation
11. [11]
    W3C (2009). SKOS Simple Knowledge Organization System Reference.W3C Recommendation
12. [12]
    W3C (2012). OWL 2 Web Ontology Language Profiles, Second Edition.W3C Recommendation
13. [13]
    Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., and Dyer, C. (2016). Neural Architectures for Named Entity Recognition. NAACL HLT, 260–270.Peer reviewed paper
14. [14]
    Devlin, J., Chang, M. W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL HLT.Peer reviewed paper
15. [15]
    Wu, L., et al. (2020). Scalable Zero-shot Entity Linking with Dense Entity Retrieval. EMNLP.Peer reviewed paper
16. [16]
    Eberts, M., and Ulges, A. (2020). Span-Based Joint Entity and Relation Extraction with Transformer Pre-Training. ECAI.Peer reviewed paper
17. [17]
    Wadden, D., Wennberg, U., Luan, Y., and Hajishirzi, H. (2019). Entity, Relation, and Event Extraction with Contextualized Span Representations. EMNLP IJCNLP.Peer reviewed paper
18. [18]
    Yao, Y., et al. (2019). DocRED: A Large-Scale Document-Level Relation Extraction Dataset. ACL.Peer reviewed paper
19. [19]
    Huguet Cabot, P. L., and Navigli, R. (2021). REBEL: Relation Extraction By End-to-end Language generation. Findings of EMNLP, 2370–2381.Peer reviewed paper
20. [20]
    Mintz, M., Bills, S., Snow, R., and Jurafsky, D. (2009). Distant supervision for relation extraction without labeled data. ACL IJCNLP.Peer reviewed paper
21. [21]
    Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O. (2013). Translating Embeddings for Modeling Multi-relational Data. NeurIPS.Peer reviewed paper
22. [22]
    Yang, B., Yih, W. T., He, X., Gao, J., and Deng, L. (2015). Embedding Entities and Relations for Learning and Inference in Knowledge Bases. ICLR.Conference paper with accessible preprint
23. [23]
    Trouillon, T., Welbl, J., Riedel, S., Gaussier, E., and Bouchard, G. (2016). Complex Embeddings for Simple Link Prediction. ICML, PMLR 48, 2071–2080.Peer reviewed paper
24. [24]
    Sun, Z., Deng, Z. H., Nie, J. Y., and Tang, J. (2019). RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. ICLR.Peer reviewed paper
25. [25]
    Schlichtkrull, M., et al. (2018). Modeling Relational Data with Graph Convolutional Networks. ESWC.Conference paper with accessible preprint
26. [26]
    Zhu, Z., Zhang, Z., Xhonneux, L. P., and Tang, J. (2021). Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction. NeurIPS.Peer reviewed paper
27. [27]
    Lajus, J., Galárraga, L., and Suchanek, F. (2020). Fast and Exact Rule Mining with AMIE 3. ESWC.Peer reviewed paper
28. [28]
    Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The PageRank Citation Ranking: Bringing Order to the Web. Stanford technical report 1999-66.Technical report; legacy institutional record
29. [29]
    Traag, V. A., Waltman, L., and van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 9, 5233.Peer reviewed paper
30. [30]
    Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q. (2017). On Calibration of Modern Neural Networks. ICML, PMLR 70, 1321–1330.Peer reviewed paper
31. [31]
    Vrandečić, D., and Krötzsch, M. (2014). Wikidata: a free collaborative knowledgebase. Communications of the ACM, 57(10), 78–85.Published research article
32. [32]
    Himmelstein, D. S., et al. (2017). Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife, 6, e26726.Peer reviewed paper
33. [33]
    Chandak, P., Huang, K., and Zitnik, M. (2023). Building a knowledge graph to enable precision medicine. Scientific Data, 10, 67.Peer reviewed data descriptor
34. [34]
    Lewis, P., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. NeurIPS.Conference paper with accessible preprint
35. [35]
    Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., and Wu, X. (2024). Unifying Large Language Models and Knowledge Graphs: A Roadmap. IEEE Transactions on Knowledge and Data Engineering, 36(7), 3580–3599.Peer reviewed survey
36. [36]
    Edge, D., et al. (2024). From Local to Global: A Graph RAG Approach to Query-Focused Summarization. arXiv:2404.16130.Preprint; cited research version
37. [37]
    Sun, J., et al. (2024). Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph. ICLR.Conference paper with accessible preprint
38. [38]
    Luo, L., Li, Y. F., Haffari, G., and Pan, S. (2024). Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning. ICLR.Peer reviewed paper
39. [39]
    He, X., et al. (2024). G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering. NeurIPS.Conference paper with accessible preprint
40. [40]
    Yao, S., et al. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. ICLR.Conference paper with accessible preprint
41. [41]
    Guo, Z., Xia, L., Yu, Y., Ao, T., and Huang, C. (2025). LightRAG: Simple and Fast Retrieval-Augmented Generation. Findings of EMNLP, 10746–10761.Peer reviewed paper
42. [42]
    Cao, Y., Gao, Z., Li, Z., Xie, X., Zhou, S. K., and Xu, J. (2025). LEGO-GraphRAG: Modularizing Graph-Based Retrieval-Augmented Generation for Design Space Exploration. Proceedings of the VLDB Endowment, 18(10).Peer reviewed paper
43. [43]
    Dong, J., et al. (2025). Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning. arXiv:2508.19855.Preprint; findings not independently reproduced here
44. [44]
    Ma, S., et al. (2025; first posted 2024). Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation. arXiv:2407.10805, version 7.Preprint; cited research version
45. [45]
    Neo4j (version 5 documentation). Constraints. Cypher Manual.Official software documentation
46. [46]
    Neo4j (version 5 documentation). MATCH. Cypher Manual.Official software documentation
47. [47]
    Wolfram Research. LinearSolve. Wolfram Language Documentation.Official computational documentation
48. [48]
    Wolfram Research. PageRankCentrality. Wolfram Language Documentation.Official computational documentation
49. [49]
    Wilkinson, M. D., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.Peer reviewed principles paper
50. [50]
    Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys, 3, 96–146.Peer reviewed overview
51. [51]
    W3C (2014). RDF 1.1 Primer.W3C Working Group Note
52. [52]
    W3C (2013). SPARQL 1.1 Entailment Regimes.W3C Recommendation